Economic development, weather shocks and child marriage in South Asia: A machine learning approach
Stephan Dietrich, Aline Meysonnat, Francisco Rosales, Victor Cebotari & Franziska Gassmann
#2021-034
Globally, 21 percent of young women are married before their 18th
birthday. Despite some progress in addressing child marriage, it remains
a widespread practice, in particular in South Asia. While household
predictors of child marriage have been studied extensively in the
literature, the evidence base on macro-economic factors contributing to
child marriage and models that predict where child marriage cases are
most likely to occur remains limited. In this paper we aim to fill this
gap and explore region-level indicators to predict the persistence of
child marriage in four countries in South Asia, namely Bangladesh,
India, Nepal and Pakistan. We apply machine learning techniques to child
marriage data and develop a prediction model that relies largely on
regional and local inputs such as droughts, floods, population growth
and nightlight data to model the incidence of child marriages. We find
that our gradient boosting model is able to identify a large proportion
of the true child marriage cases and correctly classifies 78% of the
true marriage cases, with a higher accuracy in Bangladesh (90% of the
cases) and a lower accuracy in Nepal (71% of cases). In addition, all
countries contain in their top 10 variables for classification nighttime
light growth, a shock index of drought over the previous and the last
two years and the regional level of education, suggesting that income
shocks, the regional economic activity and regional education levels
play a significant role in predicting child marriage. Given the accuracy
of the model to predict child marriage, our model is a valuable tool to
support policy design in countries where household-level data remains
limited.
Keywords: child marriage, income shocks, machine learning, South Asia
JEL Classification: J1, J12, Q54, R11